application of forecasting technique for students · pdf fileapplication of forecasting...

Knowledge Management International Conference (KMICe) 2014, 12 – 15 August 2014, Malaysia

http://www.kmice.cms.net.my/ 655

Application of Forecasting Technique for Students Enrollment

�orhaidah Abu Haris1,Munaisyah Abdullah

2, Abu Talib Othman

3and Fauziah Abdul Rahman

4

1Universiti Kuala Lumpur (U*IKL), Malaysia,[email protected] 2Universiti Kuala Lumpur (U*IKL), Malaysia,[email protected]

3Universiti Kuala Lumpur (U*IKL), Malaysia,[email protected] 4Universiti Kuala Lumpur (U*IKL), Malaysia,[email protected]

ABSTRACT

Students enrollment forecasting provides

information for management in decision making and

planning for the Higher Education Institutions

(HEI). Due to that, many forecasting techniques

have been proposed to improve the accuracy in the

predictions. This study discussed the factors that

influence the students’enrollment and various

forecasting techniques that have been applied in

several studies.The understanding offorecasting

techniques is important to find which techniques

could give the best result and could contribute

informative knowledge, especially for management

to make decisions in HEI students' enrollment area.

Keywords:enrollment, forecasting, decision

making.

I I�TRODUCTIO�

Forecasting is an important tool for both strategic

and tactical decision making that is considered as an

essential part for efficient and effective

management. The reasons for management to

considerforecasting are to study the current,

historical and the continuance of data relationships

for certain situations, where that forecasting

techniques that will providea high level of accuracy

with less cost, the quantity of the historical data in

which the relevancy of the information can be

measured for reliable decision making and lastly,

the time frame to formulate the forecast.(Dianne &

Amrik S., 1994)

Forecasting will help management to make further

assumptions based on data collected. Enrollment

forecasting is related to analyzing current trends,

understanding the significant impact on the

enrollment and revenue outcomes. This information

could be used to influence the future strategy and

resource decisions. (Ward, 2007)

Student enrollment forecastingprovides important

information for management decision making and

planning in Higher Education Institution (HEI).

The significance of this study is tounderstand

various factors that influencestudents to enroll in

HEI. Two forecasting techniques are identified in

this study – Statistical and Data Mining shows that

each technique has their own strengths and

weaknesses to produce forecast accuracy.Some of

the influential factors have been used by the

researchers to forecast the students’ enrollment

decisions in HEI. The selection of HEI for students’

enrollment could be varied between different HEI

and different countries. Therefore, factors contribute

in influencing the students to enroll also could be

deferred.

Therefore, some of the knowledge contribute from

the chosen forecast techniques is dependent on the

selection factors of the particular HEI.

II FACTORS I�FLUE�CE STUDE�TS

E�ROLLME�T I� HEI

Studies on students’ decisions have attracted many

researchers. The influencingfactorsthat lead toa

conclusion on the student enrollment decisions can

be divided into three main factors that can be further

categorized into eleven sub-factors. The factors are:

(Farhan et al, 2012)

a. Internal Factors – Aspiration, Aptitude and

Career

b. External Factors – Courses, Cost Location,

Reputation, Promotion and Facilities.

c. Social Factors – Parents, Friends and Teachers.

The factors influencing the students’ choice to study

in HEI are important because HEI will be able to

understand the reasons why a student chooses a

particular institution over others. The information

obtained can be used by universities to assist in the

development of a marketing plan.(Mahsood &

Chenicheri, 2010) Therefore the management



couldlayout strategic planning to promote their HEI

to potential students’.

Other factors that could influence the students’

enrollment decisions in a particular HEI are:

a. Financial Aid - financial aid merit awards

provided by the HEI.(Klaauw, 2002)(James,

2009)(Eric et al, 2009), the availability of

financial aid can affect enrollment and

completion(Deland & Cochrane, 2008). The

importance of financial aid is an effective tool to

compete with other colleges to obtain

students.(Klaauw, 2002) The types of financial

aid – merit based scholarships, need based

scholarship and educational loan(Janet, 2007).

The choice of public or private depends on the

financial aid offered for students from low

income families.(Hashem & John A., 2010)

b. Information Satisfaction - the information

regarding product or service provided by the HEI

(Farhan et al, 2012).Students’ enquiry on the

institutions can also predict the enrollment

decision(Monks, 2009).

c. Employment Opportunities – employment

opportunities upon completion of studies.

(Klaauw, 2002).

d. University or College choice – depends on the

facilities, procedures and policies, entry

requirement and extra-curricular

activities.(Samsinar et al , 2003)(Wagner &

Pooyan, 2009) and quality of services

provided.(Wagner & Pooyan, 2009)

e. Tuition fee - Cost of education and degree

(content and structure) (Wagner & Pooyan,

2009)

f. Institution Prominence – The reputation of the

HEI and desired programmes(Fernandez, 2010),

quality of education and also pecuniary factors.

(Fernandez, 2010)

g. Student Characteristics, External Influences and

College Attributes(Ming, 2010)

h. Location - Geographic and demographic factors

also influence the students' enrollment decision

in HEI. (Monks, 2009)

Besides all the factors mentioned above, the main

reasons students pursue higher education were to

improve job prospects, gain knowledge and

experience. (Fernandez, 2010).Other factors that

could influence student’s decisions are facilities and

infrastructure (Ward, 2007).The studentinquiries in

choosing the HEI of their choice can also be used as

a predictor.(Cullen F. & Kenton, 2006). (Ming,

2011) in his studies emphasize that students from

different countries have different criteria in selecting

HEI. He also introduces a conceptual framework

which shows the factors that influencing student

decision to enroll in HEI in Malaysia.(Ming, 2010)

Figure 1. Conceptual Framework (Ming,2010)

In figure 1, the conceptual framework consistsof independent variables and dependent variables.The independent variables are divided into two: Fixed College Characteristics and College Effort to Communicate with students.

III FORECASTI�G TECH�IQUES O�

STUDE�T E�ROLLME�T

Forecasting techniques can be classified as either

extrapolation– extend present trends or normative –

look backward for a desired future.Technology for

forecasting techniques can be categorized

into:(Roper et al, 2011)

a. Direct – forecast parameters that measure

functional capacity or relevant characteristics.

b. Correlative – correlate parameters that measure

technology with parameters of other

technologies or other background parameters.

c. Structural – interaction between technology and

context that must be explicit.

The enrollment forecasting methods are critically

important for the HEI to determine short and long

term forecasts for capital improvement and facilities

planning. Accurate forecasts are important in times

of change. The increasing and decreasing number of

students will not only affect planning decisions, but

could givemanagement perceived quality of

instructions.(Sweeney & Middleton, 2005)

Various techniques used by HEI to forecast

enrollment. The forecasting techniques are divided

into a few categories: (Ahrens, 1979) (i) quantitative

techniques based on historical data (ii) contribution

models that incorporate the historical data and rely

on a relationship between enrollments with other

parameters, or using techniques that integrate

subjective judgment instead of using quantitative

measures; (iii) modification or suggestionsfor



adjustments or comparisons to previously developed

forecasting techniques (iv) qualitative techniques

using surveys to determine potential students’

enrollment(Nurlida, Faridah , & Nooraini, 2013).

However the selection of appropriate forecast

techniques is dependent on the availability of the

HEI data. The techniques involved in each

forecasting will give input on the long and short

term range planning of the HEI.

The field areas that are identified for HEI forecast:

(Ward, 2007)

a. Size Scope – number of students projection

for particular year or semester, target goal

achievement for HEI, influence on student

credit and tuition revenue.

b. Profile on the Students Enrollment – the

number of students that pursuing degree and

completion.

c. New Student academicprofile ability -

entrance exam scores, grade point average

and etc.

d. Enrollment Mix – gender, ethnicity,

international status, registration status (full

time vs part time), lodging status

(residential vs commuter)

e. Enrollment Outcome – persistence rate for

first time student enrollment enhancement,

student retention sustained or improved.

f. Financial Aid and Revenue – can support

the size, enrollment mix and enrollment

outcomes.

The need for forecasting as explained by (Huang &

Yu, 2013)for situations such as:

a. Difficult for decision makers to evaluate the

complexity of organizational and environmental

factors without systematic assistance.

b. The organization's relationship could be not

reliable, therefore, need forecasts to identify new

relationship.

c. Organizations could be moving toward systematic

decision making therefore need formal

forecasting to support and assess their activities.

d. Practitioners could have developed and directly

applied formal forecasting methods.

Therefore, forecasting will enable decision makers

to focus on the variety and complexity of the

problem. The selection of the correct technology

forecasting techniques for a particular problem will

help decision makers to understand better

possibilities. The following methods have been

identified to forecast on the student enrollment in

HEI and theresults weredependent on HEI

forecasting criteria.

A. Statistical

A few statistical methodshave been used to forecast the student enrollment in HEI with varyingresults.

Structural Equation Modelling (SEM) is used to identify the factors that are responsible for student choice in private HEI - experienced or qualified or knowledgeable lecturers, suitable syllabus. The finding has shown that the academician’s expertise and promotion medium will attract students to choose HEI for their educational studies. (Osman M. Zain et al, 2013)

Forecasting using Regression Analysis was applied tothe number of students enrolled by semester and level, freshman and senior as well as student demand for theme courses seats in the general education program in Grand Valley State University. The results on the forecasted historical data has been used to change on the academic policy. (Shabbir Choudhuri et al, 2007)

Institutional Research Intelligence (IRI) uses SAS Logistic Regression to predict the students that were offered admissions to HEI but turned down the offer. (Djunaidi, 2012)

The Monte Carlo method is used to forecast the

enrollment for KIMEP University, Kazakhstan. In

the studies, the findings mention that the student

choice satisfaction depends on the information

satisfaction and the information satisfaction has no

direct impact on institution choice

satisfaction.(Quinn, 2013)

Multiple Regression Analysis was used in the study

hadfocused on the Internal, External and Social

factors that influence students' choice decisions to

enroll in HEI in Pakistan. The objective of the study

is to explore factors that influence students'

decisionsby applying multiple statistical techniques

to find out association between them. (Farhan et al,

2012)

The study conducted by (Shuqin, 2002) examined

three enrollment projection methods and tested on

six community colleges and compared using Mean

Absolute Percent Error (MAPE). These methods are

Linear Regression, Auto-Regression and Three

Component Model that was applied using different

variables and which results islesser percentage of

error.Linear Regression was applied to student

population, college budget and student fees. Auto-

Regression was applied to population of ages 16 to

55, college budget and student fees. The Three

Component Model was tested on four groups of new

students (first time and non-credit students)with the

age population that are between the ages of 18-19 ,

20-24, 25-34 and 35-55.The results of the studies



show that the three methods did quite well and the

error rate is low.

Mindanao State University (MSU-IIT) used

students’data to forecastthe number of courses to be

opened before the enrollment commences. This

study was conducted using statistical time series

models – Simple Moving Average, Single

Exponential Smoothing and Double Exponential

Smoothing. The results of the time series models

were compared with naïve model used by the

university and the projected data yields less error

using MAPE with 20.5% difference in favor of the

time series models.(Rabby Q. & Mary Jane, 2012)

All these methods were used to forecast the

enrollment of students in HEI with different criteria

and variables. These methods are satisfied for

certain conditions only.

B. Data Mining

Data Mining(DM) can also be used for forecasting.A

few of data miningtechniques has been identified to

forecast the studentenrollment in HEI.

The techniques of DM as described by (Fadzillah &

Mansour Ali, 2011) could be either predictive and

descriptive. The predictive techniques is to forecast

about values of data values using known results

found from different data sources while descriptive

techniques identify patterns or relationships in data.

The Predictive techniques consist of Classification,

Prediction, Regression and Time Series analysis. The

Descriptive techniques compriseof Clustering,

Summarizations, Association Rules and Sequence

Analysis.

In the study for student enrollment at Sebha

University in Libya, the cluster analysis is used to

group the analyzed data of the undergraduate student

enrollment into clusters based on similarities

consists of genders,Libyan students, courses and

etc.The result of is used as a predictor experiment to

the predictive Analysis encompass of Neural

Network, Logistics Regression and Decision Tree

which are applied on student enrollment. The

outcomes of the studies show that Neural Network

has given better prediction accuracy on the student

enrollment.(Fadzilah & Mansour, 2009)

In other research conducted by a private university in

Thailand, Bayesian Network of DM methods had

been applied to predict student course registration in

a private university in Thailand. The data were

obtained from student enrollments together with a

GPA and grades of the subjects. The results were

used to advise students in planning to register

courses in each semester. (Pathom et al, 2008)

The study later was further enhanced by comparing

Bayesian Network with 3 other types of classifying

algorithms- C4.5, Decision Forest and NBTree using

the same variables GPA and grades of the subjects.

NBTree was concluded to give highest prediction

power. (Pathom et al, 2008)

Other DM methods which are used to predict on the

enrollment of students in the California State

University areSupport Vector Machine and Rule-

based predictive techniques.The students are

grouped into three categories: new students include

freshman and transfer, continued and returned

students. The analyzed student’s data include

population, employment, tuition and fees. The result

was analyzed by Cubist a tool to better understand

the enrollment projection.(Svetlana et al, 2006)

The research conducted by University of Lima, Peru

is to find the impact on the academic performance

based on the students’ enrollment. Two synthetic

attributes were used – course difficulty and potential

(grades in related course). C.45, K-Nearest Neighbor

(K-NN), Naïve Bayes, Bagging and Boosting were

applied to the data. The result suggests that Boosting

is the best accuracy predictor methods which was

further tested on the Student Performance

Recommender System with a learning engine with

proven outcomes.(Vialardi, et al., 2011)

Another DM technique to forecast on the academic

performance using enrollment data is Decision Tree.

The studied result focuses on the students’ strength

at the time of enrollment, therefore after the

enrollment process; HEI will attempt to improve the

quality of the student. The input will help the

instructors to take necessary actions for the slow

learners to improve in the university

examinations.(M Narayana & M., 2012)

(Borah et al, 2011)hadproposed a new attribute

Selection Measure Function (heuristic) on existing

C4.5 Decision Tree algorithm. The C4.5 gives

promising solution on the split information of the

enrollment process at the engineering college in

India.Another approach in the studies is to find the

accuracy rate between C5.0 Decision Tree algorithm,

and back propagation algorithm of the Artificial

Neural Network(ANN). The resultsshowed that the

ANNhas given the highestforecasting accuracy.

The study that was conducted at the University of

California suggested that DM techniques are much

better than statistical methods to assist HEI in

achieving enrollment goals. SAS Enterprise Miner

software is appliedto test the DM techniques, Neural

Network, Decision Tree, Logistic Regression and



Ensemble Models. From this study Neural Network

method is better than other methods(Tongshan).

Studies on enrollment forecasting also has been

conducted using Fuzzy Time Series methods. These

methods are:High Order Fuzzy Time Series. (Chen

& Hsu, 2004), Fuzzy Time Series and Genetic

Algorithms (Chen & Chung, 2006), Fuzzy Time

Series and Clustering Techniques (Kurniawan &

Chen, 2009), Adaptive Time Variant Fuzzy Time

Series(Wong, Enjian, & Chu, 2010), High Order

Fuzzy Time Series(S.M.Chen, 2002), First-Order

Fuzzy Time Series (Melike & Konstantin, 2005),

Heuristic Time Variant Fuzzy Time Series (Melike

& Konstantin, 2005), Cardinality Fuzzy Time Series

(Chang et al, 2007)were applied to find highest

forecasting accuracy.Another method is Weight

Fuzzy Time Series with additional data from

University Teknologi Malaysia(UTM).(Z. Ismail &

R.Efendi, 2011)

These Fuzzy Time Series techniques have been

conducted using enrollment data of theUniversity,

Alabama, which was originally introducedby Song

and Chissom.(Q.Song & B.S. Chissom, 1993)

Various DM forecasting techniques explained above

show that DM could be applied to improve the

accuracy inforecasting. The researchers have

proposed various techniques with different factors

and variables to suit with their forecast criteria. The

accuracy of each of the techniques is dependent on

various factors chosen for particular studies.

The studies are further categorized into the table as

shown in Table 1- the list of the Statistical methods

and Table 2 - the list of DM methods that was used

to forecast on student enrollment in HEI.

Table 1: Statistics

Statistical Methods Factors

Structural Equation

Modelling(SEM)

Academicians

Lecturers,

promotions

Regression Analysis

New+ existing

students,

courses

Monte Carlo Information

satisfaction

Multiple Regression

Internal,

External &

Social factors

Linear Regression

Student

population,

budget, fees

Auto Regression Student age,

budget & fees

Three Component

Model Student age

Statistical Time

Series –

a. Simple Moving

Average

b. Single

Exponential

Smoothing

c. Double

Exponential

Smoothing

Number of

courses to be

open

SAS – Logistic

Regression

Reject

admission

Table 2: Data Mining

Data Mining

Methods Factors

Neural Network Undergraduate

students

cluster(genders,

courses & etc)

Logistic

Regression

Decision Tree

Bayesian

Network

GPA & grades,

subjects – course

register

C4.5

Decision Forest

NB Tree

Support Vector

Machine (SVM)

New (freshman &

transfer),

continues&

returned

Selection

Measure

Function

(Heuristics) –

C4.5, Decision

Tree

Students

Enrollment

process Artificial Neural

Network – Back

propagation

Algorithm

SAS Enterprise

Miner – Neural

Network,

Decision Tree,

Logistic

Students

Enrollment



Regression,

Ensemble Model

Decision Tree Students’

Strengths

Fuzzy Time

Series (FTS),

High Order FTS,

FTS & Genetic

Algorithm,

FTS &

Clustering

Algorithm,,

Adaptive Time

Variant FTS.

First Order FTS,

Heuristic Time

Variant FTS,

Cardinality FTS,

Weight FTS

Number of

student

enrollments per

year

C4.5,

K- Nearest

neighbor,

Naïve Bayes,

Bagging &

Boosting

Course difficulty

& Grades

IV THE IMPORTA�CE OF

FORECASTI�G O� THE STUDE�TS

E�ROLLME�T

The knowledge learned from the forecasting

techniques could be important to explore the

potential of the organization’s historical data and

could be used to deal with management decisions of

the present situation(Jing, 2002).Knowledge

Management (KM) forecasting technologies could

also give significant impact on supporting the

technology of forecasting. These technologies

are:(Marc et al, 2006)

a. Data Mining – identified certain pattern of

information such as classification and

association analysis.

b. Case-Based Reasoning – support decisions and

solve problems.

c. Information Retrieval – target of the raw

information and the resulting information is

retrieved through references or alike or need to

be completely revised.

d. Topic Maps – provide methods to navigate

associative across large amounts of available

information in a conscious manner, enabling

systematic identification of information and

creation of new knowledge by the user.

e. Ontologies – related to meta-context approach in

which the explicit contextual information and

specific references have added to the information

resources.

These KM forecasting can be supported by one or more technology, which is part of strategic innovation management.

The problem of forecasting the data is very time consuming and usually involves various steps to define, extracted, cleaned, harmonized and prepared data. The reasons several techniques applied to these data is to produce a high quality forecast with best result. Understanding the factors that could drive the enrollment demand will help the HEI to derive waysto leverage numerous resources into actionable strategies that can directly impact profitability.

The choice of a good forecast technology could

either be quantitative, qualitative or mixture of both.

The rationale for selecting many different

approaches as practical within resource limitations

is to support the decision makers to make good

decision based onthe forecastinformation from a

range of desirable futures or avoid the less desirable

ones. This forecast technique will give impact on the

forecast assessment and at the same time could

affect new developments on the technologies,

processes, policies, organizations and other factors

in which forecaster can identify areas that would

give significant impacts that will occur, likelihood

and their consequences in future. ( Roper et al,

2011)

V CO�CLUSIO�

In this paper, a few methods are presented and

described in the literaturestudieson various research

that are related to forecastingon the student

enrollment in HEI. The forecasting techniques are

critical components for analyzing the consequences

and necessary to be applied in future works. Finally,

the ability to understand some of the forecasting

techniquesis important in finding the best and

suitable approach that could contribute

knowledgeable information especially in HEI

students'enrollment area.

ACK�OWLEDGME�T

We would like to express our thanks especially to

Universiti Kuala Lumpur (UniKL) for the first

author to take a PhD research.



REFERE�CES

Ahrens, S. W. (1979). Student Enrollment Forecasting

Techniques for Higher Education. Institute of

Education Science, 33.

Borah, M. D., R. J., Daya Gupta, & Deka, G. C. (2011).

Application of Knowledge Based Decision Technique

to Predict Student Enrollment Decision. 2011

International Conference on Recent Trends in

Information System, 180-184.

C. G., & K. P. (2006). A Predictive Model of Inquiry to

Enrollment. Research in Higher Education, 935-956.

Chang, J. R., Lee, Y. T., Liao, S. Y., & Cheng, C. H.

(2007). Cardinality Based Fuzzy Time Series for

Forecasting Enrollments. Springer-Verlag, 753-744.

Chen, S. M., & Chung, N. Y. (2006). Forecasting

Enrollments of Students by Using Fuzzy Time Series

and Genetic Algorithms. Information Management

Sciences, 1-17.

Chen, S. M., & Hsu, C. C. (2004). A New Method to

Forecast Enrollments using Fuzzy Time Series.

International Journal of Applied Science and

Engineering, 234-244.

D. C., & Cochrane, C. F. (2008). Paving the Way: How

Financial Aid Awareness affects College Access &

Success. Institute for College Access & Success.

D. W., & A. S. (1994). Forecasting: The Key to

Managerial Decision Making. Management Decision,

41-49.

E. B., Long, B. T., P. O., & L. S. (2009). The Role of

Simplification and Information in College Decisions:

Result from the H&R Block FAFSA Experiment.

*ational Bureau of Economics Research.

F. M., S. M., & N. B. (2012). Factors Influencing

Student's Enrollment Decisions in Selection of Higher

Education Institutions (HEI's). Interdisciplinary

Journal of Contemporary Research in Business, 558-

568.

F. S., & M. A. (2009). Uncovering Hidden Information

Within University's Student Enrollment Data Using

Data Mining. 2009 Third Asia International

Conference on Modelling & Simulation, 413-418.

F. S., & M. A. (2011). Mining Enrollment Data Using

Descriptive and Predictive Approaches,. In P. K.

(Ed.), Knowledge-Oriented Applications in Data

Mining (pp. 1-21). In-Tech.

Fernandez, J. L. (2010). An Exploratory Study of Factors

Influencing the Decision of Students to Study at

University Sains Malaysia. Kajian Malaysia Journal

of Malaysian Studies,28, no.2, 107-136.

H. D., & J. K. (2010). Enrollment at Highly Private

Colleges: Who is Left Behind? Contemporary

Economic Policy, 28(1), 94-109.

Huang, K. H., & Yu, T. K. (2013). Forecasting Regime

Switches to Assist Decision Making. Management

Decision, Emerald, 1-9.

J. C., S. M., & D. S. (1971, July). How to Choose the

Right Forecasting Technique. Retrieved March 2014,

from Harvard Business Review:

http://hbr.org/1971/07/how-to-choose-the-right-

forecasting-technique/ar/4

J. L. (2002). Data Mining and Its Application in Higher

Education. In *ew Directions for Institutional

Research. Wiley Periodicals.

J. R., & R. R. (2010). Applications of Data Mining

Techniques in Higher Education in India.

International Conference on Innovation in Redefining

Business Horizons (pp. 1-10). Journal of Knowledge

Management Practice, Vol 11.

J. W. (2007). Forecasting Enrollment to Achieve

Institutional Goals. College and University Journals,

41-46.

K. T., & Chen, S. M. (2009). A New Method to Forecast

Enrollments using Fuzzy Time Series and Clustering

Techniques. Proceedings of the Eighth International

Conference on Machine Learning and Cybernetics

(pp. 3026-3029). Baoding: IEEE.

K. W., & Fard, P. Y. (2009). Factors Influencing

Malaysian Students' Intention to Study at a Higher

Educational Institution. E-Leader Kuala Lumpur, 1-

12.

Klaauw, W. V. (2002). Estimating The Effect of

Financial Aid Offers on College Enrollment:A

Regression-Discontinuity Approach. International

Economic Review, 1249-1287.

M. H., S. S., & G. R. (2006). Evaluation of Knowledge

Management Technologies For the Support of

Technology Forecasting. 39th Annual Hawaii

Internationall Conference on System Sciences (pp. 1-

10). Systems Sciences.

M. S., & C. N. (2010). Enrolling in Higher Education:

The Perception of Stakeholders. Journal of

Institutional Research, 9-15.

M. S., & K. D. (2005). A New Trend Heuristic Time-

Variant Fuzzy Time Series Method for Forecasting

Enrollments. Computer and Information Sciences-

ISCIS, 553-564.

M. S., & K. D. (2005). Forecasting Enrollment Model

Based on First Order Fuzzy Time Series. In World

Academy of Science,Engineering and Technology,

375-378.

M. S., & M. H. (2012). Predicting Student Success from

Student Enrolment Data using Decision Tree

Technique. International Journal of Applied

Information Systems (IJAIS), 1-6.

Melike Seh, & K. D. (2005). Forecasting Enrollment

Model Based on First-Order Fuzzy Time Series.

Proceedings of World Academy of Science,

Engineering and Technology (pp. 375-378). WASET.

Ming, J. K. (2010). Institutional Factors Influencing

Students Choice Decision in Malaysia: A Conceptual

Framework. International Journal of Business and

Social Science, 53-58.

Ming, J. K. (2011). A Model of Higher Education

Institutions Choice in Malaysia - A Conceptual

Approach. International Proceedings of Economics

Development & Research.



Monks, J. (2009). The Impact of Merit Based Financial

Aid and Price Illusion on College Enrollment: A Field

Experiment. Elsevier, 99-106.

N. I., F. H., & N. M. (2013). International Students

Satisfaction Formation - Linkage Between

Information Satisfaction and Choice Satisfaction:

Study on Private Higher Education Institutions in

Malaysia. AFBE Journal, 88-108.

O. Z., M. J., & A. I. (2013). Factors Influencing Students'

Decisions in Choosing Private Institutions of Higher

Equations in malaysia: A Structural Equation

Modelling Approach. Asian Academy of Management,

1-16.

P. P., A. S., P. P., & S. N. (2008). Using Bayesian

Network for Planning Course Registration Model for

Undergraduate Students. Second IEEE International

Conference on Digital Ecosystems and Technologies

(IEEE DEST 2008), 492-496.

P. S., K. S., & S. E. (2012). Forecasting Enrollments

based on Fuzzy Time Series with Higher Forecast

Accuracy Rate. International Journal of Computer

Technology & Applications, 957-961.

Pathom Pumpuang, A. S., & P. P. (2008). Comparison of

Classifier Algorithms: Bayesian Network, C4.5,

Decision Forest and NBTree for Course registration

Planning Model of Undergraduate Students.

International Conference on Systems, Man and

Cybernetics, 3467-3651.

Q.Song, & B.S. Chissom. (1993). Fuzzy Time Series and

Its Models. Fuzzy Sets and Systems, Vol. 54, 269-277.

Quinn, M. (2013). Analyzing Long term University

Enrollment Dynamics using Monte Carlo Method.

AFBE journal.

R. L., & M. B. (2012). Enrollment Forecasting for School

Management System. International Journal of

Modelling and Optimization, 563-566.

Roper, A. T., S. C., A. P., T. M., F. R., & J. B. (2011).

Forecasting and Management of Technology. New

Jersey: John Wiley & Sons.

S. C., C. S., C. G., & W. W. (2007). Enrollment

Forecasting for an Upper Division General Education

Component. ASEE/IEEE Frontiers in Education

Conference, 25-28.

S. G. (2002). Three Enrollment Forecasting Models:

Issues in Enrollment projection for Community

Colleges. 40th RP Conference, (pp. 1-9). California.

S. M., S. H., & T. H. (2003). An Explatory Study of

Factors Influencing the College Choice Decision of

Undergraduate Students in Malaysia. Asia Pacific

Management Review, 259-280.

S.M.Chen. (2002). Forecasting Enrolments based on

High Order Fuzzy Time Series. Cybernetics and

Systems: An International Journal, 1-18.

Svetlana S. Aksenova, Du Zhang, & Meiliu Lu. (2006).

Enrollment Prediction through Data Mining. IEEE

International Conference on Information Reuse and

Integration, 510-515.

Sweeney, S. H., & Middleton, E. J. (2005). Multiregional

Cohort Enrollment Projections: Matching Methods to

Enrollment Policies. Population, Space and Place,

11(5), 361-380.

T. C. (n.d.). Data Mining: A Magic Technology for

College Recruitment. 1-18.

Vialardi, C., Chue, J., Peche, J., Alvarado, G., Estrella, J.,

& Ortigosa, A. (2011). A Data Mining Approach to

Guide Students through the Enrollment Process based

on Academic Performance. User Modeling and User

Adapted Interaction, 217-248.

Wagner, K., & P. Y. (2009). Factors Influencing

Malaysian Students' Intention to Study at a Higher

Educational Institution. E-Leader Kuala Lumpur, 1-

12.

Wong, W. K., E. B., & Chu, A. W. (2010). Adaptive

Time Variant Models for Fuzzy Time Series

Forecasting. IEEE Transaction on Systems, Man and

Cybernetics (pp. 1531-1541). IEEE.

Z. Ismail, & R.Efendi. (2011). Enrollment Forecasting

Based on Modified Weight Fuzzy Time Series.

Journal of Artificial Intelligence, 111-118.

application of forecasting technique for students · pdf fileapplication of forecasting...

Documents